Several methods have been proposed based on utilizing private information stored in clients to enhance the global model, without disclosing sensitive local data samples [1]}. Among these, federated learning has proven to be particularly thriving in training high-precision predictive models from decentralized data [2]}. Despite its remarkable contribution towards preserving privacy while achieving accurate practical models, so far, federated paradigm has mostly been applied for machine learning tasks and barely intersects with causal discovery.
